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Artificial Intelligence Models for Financial Time Series

Author

Listed:
  • Alina Barbulescu

    (Transilvania University of Brașov)

  • Cristian Stefan Dumitriu

    (SC Utilnavorep SA)

Abstract

Modeling and predicting the evolution of financial series has become an essential research domain for scientists and practitioners in the field of economics or finance. In this context, the purpose of this article is to determine two artificial intelligence alternative models for NYSE monthly series recorded for 53 years and to compare their performances.

Suggested Citation

  • Alina Barbulescu & Cristian Stefan Dumitriu, 2021. "Artificial Intelligence Models for Financial Time Series," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 685-690, August.
    • Handle: RePEc:ovi:oviste:v:xxi:y:2021:i:1:p:685-690
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    File URL: https://stec.univ-ovidius.ro/html/anale/RO/2021/Section%205/1.pdf
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    References listed on IDEAS

    as
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    5. Wun-Hua Chen & Jen-Ying Shih & Soushan Wu, 2006. "Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 1(1), pages 49-67.
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    Cited by:

    1. Alina Bărbulescu & Cristian Ștefan Dumitriu, 2021. "On the Connection between the GEP Performances and the Time Series Properties," Mathematics, MDPI, vol. 9(16), pages 1-19, August.

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    More about this item

    Keywords

    Time series; GEP; SVR; MSE; MAE; MAPE;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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